phosphoDataset msqrobPTM without GP
library(QFeatures)
library(msqrob2)
library(dplyr)
library(tidyr)
library(tibble)
library(gt)
library(plotly)
library(stageR)
library(poolr)
library(RColorBrewer)
library(seqinr)
library(stringr)
library(ExploreModelMatrix)
library(data.table)1 Import data
df <- read.csv("evidence.txt",header=TRUE, sep = ",")
#I can ignore the sigma proteins (those were just used for QC purposes)
df <- df %>% filter(!grepl("Sigma", df$Proteins))
#filter out phospho sites with less than 75% probability
#(other filtering options are possible)
filter_rows <- sapply(df$Phospho..STY..Probabilities, function(x){
if (!x==""){
prob <- str_extract_all(x, "(?<=\\().+?(?=\\))")[[1]]
if (!any(as.double(prob) > 0.75)){return(which(df$Phospho..STY..Probabilities == x))}
}
}, USE.NAMES = F)
filter_rows <- unique(unlist(filter_rows))
nrow(df)## [1] 178235df <- df[-filter_rows,]
nrow(df)## [1] 171780#Now get format into wide format
df_wide <- pivot_wider(df, id_cols = c("Sequence", "Modifications", "Modified.sequence", "Proteins", "Leading.proteins",
"Reverse", "Potential.contaminant", "Protein.group.IDs", "Leading.razor.protein"),
#Is dat hier de beste manier om met
#gedupliceerde features om te gaan?
names_from = "Raw.file", names_prefix = "Intensity_", values_from = "Intensity", values_fn = max)ecols <- grep("Intensity",colnames(df_wide))
#order dataframe by protein, for the normalisation step
df_wide = df_wide[order(df_wide$Leading.razor.protein),]
pe <- readQFeatures(df_wide,ecol= ecols,name="peptidoformRaw")rownames(pe[["peptidoformRaw"]]) <- rowData(pe[["peptidoformRaw"]])$Modified.sequence1.1 Experimental Layout
metadata <- read.csv("Experimental Design.csv")colData(pe)$file <- sapply(str_split(rownames(colData(pe)), "_"), function(x) x[[2]])
metadata$File <- sub(x=metadata$File, pattern=".raw",replacement = "")
colData(pe)$condition <- sapply(colData(pe)$file, function(x){
metadata[metadata$File==x,]$Condition
}, USE.NAMES = F)
colData(pe)$subset <- sapply(colData(pe)$file, function(x){
metadata[metadata$File==x,]$Subset
}, USE.NAMES = F)
colData(pe)$condition <- case_when(grepl("A", colData(pe)$condition) ~ "A",
TRUE ~ "B")
colData(pe)## DataFrame with 90 rows and 3 columns
## file condition subset
## <character> <character> <character>
## Intensity_QX21595GM QX21595GM A y
## Intensity_QX21643GM QX21643GM A y
## Intensity_QX21469GM QX21469GM B x
## Intensity_QX21484GM QX21484GM B x
## Intensity_QX21487GM QX21487GM A x
## ... ... ... ...
## Intensity_QX21782GM QX21782GM A y
## Intensity_QX21794GM QX21794GM A y
## Intensity_QX21803GM QX21803GM B y
## Intensity_QX21806GM QX21806GM A y
## Intensity_QX21812GM QX21812GM A yMSnbase::plotNA(assay(pe[["peptidoformRaw"]])) +
xlab("Peptide index (ordered by data completeness)")## Warning in fun(libname, pkgname): mzR has been built against a different Rcpp version (1.0.8.3)
## than is installed on your system (1.0.9). This might lead to errors
## when loading mzR. If you encounter such issues, please send a report,
## including the output of sessionInfo() to the Bioc support forum at
## https://support.bioconductor.org/. For details see also
## https://github.com/sneumann/mzR/wiki/mzR-Rcpp-compiler-linker-issue.
2 Preprocessing
rowData(pe[["peptidoformRaw"]])$nNonZero <- rowSums(assay(pe[["peptidoformRaw"]]) > 0, na.rm = T)
pe <- zeroIsNA(pe, i = "peptidoformRaw")
pe <- filterFeatures(pe, ~Reverse != "+")
pe <- filterFeatures(pe, ~Potential.contaminant != "+")
pe <- logTransform(pe, base = 2, i = "peptidoformRaw", name = "peptidoformLog")
pe <- pe[rowData(pe[["peptidoformRaw"]])$nNonZero>2,,]
pe <- QFeatures::normalize(pe, method = "center.median", i = "peptidoformLog", name = "peptidoform")
colData(pe[["peptidoform"]]) <- colData(pe)2.1 Normalisation via robust summarisation
pe <- aggregateFeatures(pe,
i = "peptidoform",
fcol = "Leading.razor.protein",
na.rm = TRUE,
name = "proteinRobust",
fun = MsCoreUtils::robustSummary)## Your quantitative data contain missing values. Please read the relevant
## section(s) in the aggregateFeatures manual page regarding the effects
## of missing values on data aggregation.#Only take pepforms that have a parent protein
#(when there is no global profiling dataset, this will be all peptidoforms)
pepWithProtein <- rowData(pe[["peptidoform"]])$Proteins %in% rownames(pe[["proteinRobust"]])
pePepWithProtein <- pe[["peptidoform"]][pepWithProtein,]
pe <- addAssay(pe,pePepWithProtein,"pepformRel")
#normalisation for protein abundance step
assay(pe[["pepformRel"]]) <- assay(pe[["pepformRel"]]) - assay(pe[["proteinRobust"]])[rowData(pe[["pepformRel"]])$Proteins,colnames(assay(pe[["pepformRel"]]))]
boxplot(assay(pe[["pepformRel"]]))
3 Differential peptidoform usage (DPFU)
3.1 Hypothesistest for each contrast
colData(pe)$condition <- relevel(as.factor(colData(pe)$condition), ref = "B")
colData(pe)$subset <- as.factor(colData(pe)$subset)pe <- msqrob2::msqrob(object = pe, i = "pepformRel", formula = ~condition*subset, robust=FALSE)## Warning: Zero sample variances detected, have been offset away from zerorowData(pe[["pepformRel"]])$msqrobModels[[2]] %>%
getCoef## (Intercept) conditionA subsety conditionA:subsety
## -0.9008228 1.3425797 0.9384266 -1.1383736fraction_of_ys <- (colData(pe) %>% as_tibble() %>% filter(subset == "y") %>% nrow()) / nrow(colData(pe))
contrasts <- c("conditionA", "conditionA + conditionA:subsety", "conditionA:subsety", "conditionA + 0.5 * conditionA:subsety", "conditionA + 0.6666667 * conditionA:subsety")
L <- makeContrast(c("conditionA = 0",
"conditionA + conditionA:subsety = 0",
"conditionA:subsety = 0",
"conditionA + 0.5 * conditionA:subsety = 0",
"conditionA + 0.6666667 * conditionA:subsety = 0"),
parameterNames = rowData(pe[["pepformRel"]])$msqrobModels[[2]] %>%
getCoef %>%
names)
pe <- hypothesisTest(object = pe, i = "pepformRel", contrast = L, overwrite = T)3.1.1 Volcanoplot
volcano <- list()
for (contrast in contrasts){
volcano[[contrast]] <- rowData(pe[["pepformRel"]])[[contrast]]%>%
ggplot(aes(x = logFC, y = -log10(pval),
color = adjPval < 0.05,
annotation=rowData(pe[["pepformRel"]])[,3])) +
geom_point(cex = 2.5) +
scale_color_manual(values = alpha(c("black", "red"), 0.5)) +
theme_minimal() +
ylab("-log10(pvalue)") +
ggtitle(contrast)
}
volcano## $conditionA## Warning: Removed 687 rows containing missing values (geom_point).
##
## $`conditionA + conditionA:subsety`## Warning: Removed 687 rows containing missing values (geom_point).
##
## $`conditionA:subsety`## Warning: Removed 687 rows containing missing values (geom_point).
##
## $`conditionA + 0.5 * conditionA:subsety`## Warning: Removed 687 rows containing missing values (geom_point).
##
## $`conditionA + 0.6666667 * conditionA:subsety`## Warning: Removed 687 rows containing missing values (geom_point).
3.1.2 Significant peptidoforms for each contrast
tables <- list()
for (contrast in contrasts){
sigTable <- rowData(pe[["pepformRel"]])[[contrast]]
if(nrow(sigTable %>%
na.exclude %>%
filter(adjPval<0.05))>0){
sigTable <- sigTable %>%
na.exclude %>%
filter(adjPval<0.05) %>%
arrange(pval) %>%
mutate(
se = format(se, digits = 2),
df = format(df, digits =2),
t = format(t, digits = 2),
adjPval = format(adjPval, digits = 3),
rank = 1:length(logFC)
)
sigTable_print <- sigTable %>% mutate(peptidoform = rownames(sigTable)) %>% gt() %>% tab_header(title = md(contrast))
tables[[contrast]] <- sigTable
}
}
knitr::kable(tables)
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4 DPTM
4.1 ptm summarisatie
4.1.1 Determine location of ptm
fasta <- "human.fasta"
parsed_fasta <- read.fasta(file = fasta, seqtype = "AA", as.string = T)#modified sequence column contains _ that does not matter, but hinders location determining
rowData(pe[["pepformRel"]])$modified_sequence <- gsub("_", "", rowData(pe[["pepformRel"]])$Modified.sequence)get_ptm_location <- function(feature, data, fasta, mod_column = "Modifications",
peptide_seq_column = "Sequence", mod_seq_column = "modified_sequence",
protein_column = "Leading.razor.protein", split = ",", collapse = ", "){
prot <- data[feature,][[protein_column]]
pep_seq <- data[feature,][[peptide_seq_column]]
mod_seq <- data[feature,][[mod_seq_column]]
prot_seq <- fasta[[prot]][1]
#find location of peptide in protein
#add -1 here, so that the addition of the location later on is correct
pep_location <- unlist(lapply(gregexpr(pattern = pep_seq, prot_seq), min)) - 1
final_mod <- c()
j <- mod_seq
#go over the modifications inside the modified sequence
for(mod in regmatches(mod_seq, gregexpr("\\(.*?\\)\\)", mod_seq, perl=T))[[1]]){
#find location of modification in peptide
mod_location <- unlist(lapply(gregexpr(pattern = mod, j, fixed = T), min))
#get location in protein (-1 because else it gives you the location after because of the presence of the modification in the string)
location <- mod_location + pep_location -1
#add location to modification
mod_ <- paste(mod, location)
#save modification
final_mod <- c(final_mod, mod_)
#now remove current modification from the sequence, so that we can continue to the next mod
str_sub(j, mod_location, nchar(mod)+mod_location-1) <- ""
}
return(paste(final_mod, collapse = collapse))
}rowData(pe[["pepformRel"]])$mod <- sapply(rownames(rowData(pe[["pepformRel"]])), function(x){
get_ptm_location(x, rowData(pe[["pepformRel"]]), parsed_fasta)
}, USE.NAMES = F)#Add ptm variable = protein + modification
rowData(pe[["pepformRel"]])$ptm <- ifelse(rowData(pe[["pepformRel"]])$mod != "",
paste(rowData(pe[["pepformRel"]])$Leading.razor.protein, rowData(pe[["pepformRel"]])$mod, sep="_"),
"")4.1.2 Get ptm level intensity matrix
Get all unique ptms present in the dataset (all protein - modification - location combinations)
prots <- unique(rowData(pe[["pepformRel"]])$Leading.razor.protein)
#Do for each protein
ptms <- sapply(prots, function(i) {
pe_sub <- pe[["pepformRel"]][grepl(i, rowData(pe[["pepformRel"]])$Leading.razor.protein, fixed = T),]
#pe_sub <- filterFeatures(pe,~grepl(Leading.razor.protein,pattern=i,fixed = T))
#Get all unique modification present on that protein
mods <- unique(unlist(strsplit(rowData(pe_sub)$mod, split = ", ", fixed = TRUE)))
#Add protein info to mods
ptm <- paste(rep(i, length(mods)), mods)
#return all the protein-mods combinations
ptm
})
ptms <- as.vector(unlist(ptms))#For each ptm do
ptm_x_assay <- sapply(seq(1:length(ptms)), function(i){
x <- ptms[i]
#Get current protein and mod from ptm
prot <- str_split(x, " ", 2)[[1]][1]
current_ptm <- str_split(x, " ", 2)[[1]][2]
#filter on that protein and on that mod to obtain all peptidoforms that correspond to the ptm
pe_sub <- pe[["pepformRel"]][grepl(prot, rowData(pe[["pepformRel"]])$Leading.razor.protein, fixed = T),]
#pe_sub <- filterFeatures(pe,~grepl(Leading.razor.protein,pattern=prot, fixed = T))
ptm_sub <- pe_sub[grepl(current_ptm, rowData(pe_sub)$mod, fixed = T),]
#ptm_sub <- filterFeatures(pe_sub,~grepl(mod,pattern=current_ptm, fixed=T))[["peptidoformNorm"]]
#Get intensity values of those peptidoforms
y <- assay(ptm_sub)
#And summarise them to 1 row of intensity values: 1 value per sample for that ptm
ptm_y <- try(MsCoreUtils::robustSummary(y), silent = T)
if (is(ptm_y, "try-error")){
ptm_y <- rep(NA, ncol(y))}
ptm_y
})## Warning in rlm.default(X, expression, ...): 'rlm' failed to converge in 20 steps
## Warning in rlm.default(X, expression, ...): 'rlm' failed to converge in 20 steps
## Warning in rlm.default(X, expression, ...): 'rlm' failed to converge in 20 steps
## Warning in rlm.default(X, expression, ...): 'rlm' failed to converge in 20 steps#Then we get the intensity assay on ptm level
ptm_x_assay <- t(ptm_x_assay)
rownames(ptm_x_assay) <- ptms4.1.3 Add to QFeatures object
Filter out ptms with all zero intensities
print(paste(nrow(ptm_x_assay), "ptms before filtering"))## [1] "610 ptms before filtering"filtering <- rowSums(ptm_x_assay != 0, na.rm=TRUE) > 0
ptm_x_assay <- ptm_x_assay[filtering,]
print(paste(nrow(ptm_x_assay), "ptms after filtering"))## [1] "471 ptms after filtering"all(rownames(colData(pe)) == colnames(ptm_x_assay))## [1] TRUErowdata <- data.frame(ptm = rownames(ptm_x_assay))
rowdata$protein <- sapply(str_split(rowdata$ptm, pattern=" "),function(x) x[1])
ptm_y_assay <- SummarizedExperiment(assays=as.matrix(ptm_x_assay), rowData=rowdata, colData=colData(pe))pe <- addAssay(pe, ptm_y_assay, "ptmRel")4.2 Hypothesistest for each contrast
pe <- msqrob2::msqrob(object = pe, i = "ptmRel", formula = ~condition*subset, robust=TRUE, overwrite = T)## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
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## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
## Warning in rlm.default(X, y, method = "M", maxit = maxitRob): 'rlm' failed to
## converge in 1 steps
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## converge in 1 stepsrowData(pe[["ptmRel"]])$msqrobModels[[2]] %>%
getCoef## (Intercept) conditionA subsety conditionA:subsety
## -0.20364594 0.02035259 -0.03977042 0.02892887contrasts <- c("conditionA", "conditionA + conditionA:subsety", "conditionA:subsety", "conditionA + 0.5 * conditionA:subsety", "conditionA + 0.6666667 * conditionA:subsety")
L <- makeContrast(c("conditionA = 0",
"conditionA + conditionA:subsety = 0",
"conditionA:subsety = 0",
"conditionA + 0.5 * conditionA:subsety = 0",
"conditionA + 0.6666667 * conditionA:subsety = 0"),
parameterNames = rowData(pe[["ptmRel"]])$msqrobModels[[2]] %>%
getCoef %>%
names)
pe <- hypothesisTest(object = pe, i = "ptmRel", contrast = L, overwrite = T)4.2.1 Volcanoplot
volcanos <- list()
for (contrast in contrasts){
library(plotly)
volcanos[[contrast]] <-
rowData(pe[["ptmRel"]])[[contrast]]%>%
ggplot(aes(x = logFC, y = -log10(pval),
color = adjPval < 0.05,
annotation=rowData(pe[["ptmRel"]])[,3])) +
geom_point(cex = 2.5) +
scale_color_manual(values = alpha(c("black", "red"), 0.5)) + theme_minimal() +
ylab("-log10(pvalue)") +
ggtitle(contrast)
}
volcanos## $conditionA## Warning: Removed 68 rows containing missing values (geom_point).
##
## $`conditionA + conditionA:subsety`## Warning: Removed 68 rows containing missing values (geom_point).
##
## $`conditionA:subsety`## Warning: Removed 68 rows containing missing values (geom_point).
##
## $`conditionA + 0.5 * conditionA:subsety`## Warning: Removed 68 rows containing missing values (geom_point).
##
## $`conditionA + 0.6666667 * conditionA:subsety`## Warning: Removed 68 rows containing missing values (geom_point).
4.2.2 Heatmap
We first select the names of the ptms that were declared signficant.
4.2.3 Significant ptms for each contrast
tables <- list()
for (contrast in contrasts){
sigTable <- rowData(pe[["ptmRel"]])[[contrast]]
if(nrow(sigTable %>%
na.exclude %>%
filter(adjPval<0.05)) > 0){
sigTable <- sigTable %>%
na.exclude %>%
filter(adjPval<0.05) %>%
arrange(pval) %>%
mutate(
se = format(se, digits = 2),
df = format(df, digits =2),
t = format(t, digits = 2),
adjPval = format(adjPval, digits = 3),
rank = 1:length(logFC)
)
sigTable_print <- sigTable %>% mutate(peptidoform = rownames(sigTable)) %>% gt() %>% tab_header(title = md(contrast))
tables[[contrast]] <- sigTable
}
}
knitr::kable(tables)
|
|
|
|
a = "sp|Q16610|ECM1_HUMAN (Phospho (STY)) 80"
b = "sp|P13611|CSPG2_HUMAN (Phospho (STY)) 2115"
tables <- list()
for (contrast in contrasts){
sigTable <- rowData(pe[["ptmRel"]])[[contrast]]
tables[[contrast]] <- sigTable %>% rownames_to_column("ptm") %>% filter(ptm==a|ptm==b)
}
knitr::kable(tables)
|
|
|
|
|
5 PLOTS
5.1 Lineplots
5.1.1 PTM - level
ptm_list1 <- c()
for (contrast in contrasts){
sigTable <- rowData(pe[["ptmRel"]])[[contrast]] %>% filter(adjPval<0.05)
ptm_list1 <- c(ptm_list1, rownames(sigTable))
}
ptm_list1 <- unique(ptm_list1)
plots <- list()
for (i in ptm_list1){
prot_ = str_split(i, " ")[[1]][1]
site_ = str_split_fixed(i, " ", 2)[,2]
pepform_df <- longFormat(pe[,,"pepformRel"], rowvars = c("Leading.razor.protein", "ptm"), colvars = c("condition", "subset")) %>% as.data.frame()
pepform_df <- pepform_df %>% filter(Leading.razor.protein == prot_)
pepform_df <- pepform_df %>% filter(grepl(site_, ptm, fixed = T))
pepform_df$FeatureType <- "Peptide"
pepform_df <- pepform_df %>% arrange(condition, subset, rowname)
ptm_df <- longFormat(pe[,,"ptmRel"], rowvars = c("protein", "ptm"), colvars = c("condition", "subset")) %>% as.data.frame()
ptm_df <- ptm_df %>% filter(ptm == i)
ptm_df$FeatureType <- "PTM"
ptm_df <- ptm_df %>% arrange(condition, subset, rowname)
ptm_estimate <- ptm_df %>% select(c("primary", "colname", "condition", "subset")) %>%
mutate(rowname = paste(ptm_df$rowname, "estimate"),
ptm = paste(ptm_df$ptm, "estimate"),
Protein = paste(ptm_df$Protein, "estimate"),
FeatureType = "PTM_estimated",
value = NA,
assay = "model")
fixeffects <- rowData(pe[["ptmRel"]])$msqrobModels[[unique(ptm_df$ptm)]] %>% getCoef
ptm_estimate[ptm_estimate$condition=="B"&(ptm_estimate$subset=="x"),]$value <-
fixeffects[["(Intercept)"]]
ptm_estimate[ptm_estimate$condition=="B"&(ptm_estimate$subset=="y"),]$value <-
fixeffects[["(Intercept)"]] + fixeffects[["subsety"]]
ptm_estimate[ptm_estimate$condition=="A"&(ptm_estimate$subset=="x"),]$value <-
fixeffects[["(Intercept)"]] + fixeffects[["conditionA"]]
ptm_estimate[ptm_estimate$condition=="A"&(ptm_estimate$subset=="y"),]$value <-
fixeffects[["(Intercept)"]] + fixeffects[["conditionA"]] + fixeffects[["conditionA:subsety"]] + fixeffects[["subsety"]]
plot_df <- rbindlist(list(pepform_df, ptm_df, ptm_estimate), fill = TRUE)
plot_points <- rbindlist(list(pepform_df, ptm_df), fill = TRUE)
plot_df$primary <- forcats::fct_inorder(plot_df$primary)
plots[[i]] <- plot_df %>% ggplot() +
geom_line(aes(x = primary, y = value , group = rowname, color = FeatureType), size = 2) +
geom_point(data = plot_points, aes(x = primary, y = value , group = rowname,
color = FeatureType), size = 5) +
geom_vline(data=data.frame(x = c(43.5, 17.5, 56.5)),
aes(xintercept=as.numeric(x)), linetype = "dashed") +
scale_colour_manual(values = c("Peptide" = "#C3C3C3", "PTM" = "palegreen3",
"PTM_estimated" = "slateblue3")) +
labs(title = i, x = "Sample", y = "Intensity") +
theme_light() +
theme(axis.text.x = element_text(angle = 60, hjust=1, size = 10),
axis.text.y = element_text(size = 16),
legend.text=element_text(size=19),
axis.title.y = element_text(size = 16),
axis.title.x = element_text(size = 16),
title = element_text(size = 19),
strip.text = element_text(size = 16),
legend.title = element_blank(),
legend.direction = "horizontal",
legend.position = c(0.5, 0.1)) +
annotate("text", x = 24, y = 6.5, label = "B", size = 8) +
annotate("text", x = 70, y = 6.5, label = "A", size = 8) +
annotate("text", x = 9, y = 6, label = "x", size = 7) +
annotate("text", x = 30, y = 6, label = "y", size = 7) +
annotate("text", x = 49.5, y = 6, label = "x", size = 7) +
annotate("text", x = 73, y = 6, label = "y", size = 7) +
ylim(-6, 6.5)
}## Warning: 'experiments' dropped; see 'metadata'## harmonizing input:
## removing 450 sampleMap rows not in names(experiments)## Warning: 'experiments' dropped; see 'metadata'## harmonizing input:
## removing 450 sampleMap rows not in names(experiments)## Warning: 'experiments' dropped; see 'metadata'## harmonizing input:
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## removing 450 sampleMap rows not in names(experiments)## Warning: 'experiments' dropped; see 'metadata'## harmonizing input:
## removing 450 sampleMap rows not in names(experiments)## Warning: 'experiments' dropped; see 'metadata'## harmonizing input:
## removing 450 sampleMap rows not in names(experiments)## Warning: 'experiments' dropped; see 'metadata'## harmonizing input:
## removing 450 sampleMap rows not in names(experiments)## Warning: 'experiments' dropped; see 'metadata'## harmonizing input:
## removing 450 sampleMap rows not in names(experiments)## Warning: 'experiments' dropped; see 'metadata'## harmonizing input:
## removing 450 sampleMap rows not in names(experiments)## Warning: 'experiments' dropped; see 'metadata'## harmonizing input:
## removing 450 sampleMap rows not in names(experiments)## Warning: 'experiments' dropped; see 'metadata'## harmonizing input:
## removing 450 sampleMap rows not in names(experiments)## Warning: 'experiments' dropped; see 'metadata'## harmonizing input:
## removing 450 sampleMap rows not in names(experiments)## Warning: 'experiments' dropped; see 'metadata'## harmonizing input:
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## removing 450 sampleMap rows not in names(experiments)## Warning: 'experiments' dropped; see 'metadata'## harmonizing input:
## removing 450 sampleMap rows not in names(experiments)## Warning: 'experiments' dropped; see 'metadata'## harmonizing input:
## removing 450 sampleMap rows not in names(experiments)## Warning: 'experiments' dropped; see 'metadata'## harmonizing input:
## removing 450 sampleMap rows not in names(experiments)## Warning: 'experiments' dropped; see 'metadata'## harmonizing input:
## removing 450 sampleMap rows not in names(experiments)## Warning: 'experiments' dropped; see 'metadata'## harmonizing input:
## removing 450 sampleMap rows not in names(experiments)## Warning: 'experiments' dropped; see 'metadata'## harmonizing input:
## removing 450 sampleMap rows not in names(experiments)## Warning: 'experiments' dropped; see 'metadata'## harmonizing input:
## removing 450 sampleMap rows not in names(experiments)## Warning: 'experiments' dropped; see 'metadata'## harmonizing input:
## removing 450 sampleMap rows not in names(experiments)## Warning: 'experiments' dropped; see 'metadata'## harmonizing input:
## removing 450 sampleMap rows not in names(experiments)## Warning: 'experiments' dropped; see 'metadata'## harmonizing input:
## removing 450 sampleMap rows not in names(experiments)## Warning: 'experiments' dropped; see 'metadata'## harmonizing input:
## removing 450 sampleMap rows not in names(experiments)## Warning: 'experiments' dropped; see 'metadata'## harmonizing input:
## removing 450 sampleMap rows not in names(experiments)plots## $`sp|O94769|ECM2_HUMAN (Phospho (STY)) 75`## Warning: Removed 13 rows containing missing values (geom_point).
##
## $`sp|O94769|ECM2_HUMAN (Oxidation (M)) 76`## Warning: Removed 22 rows containing missing values (geom_point).
##
## $`sp|O94769|ECM2_HUMAN (Phospho (STY)) 245`## Warning: Removed 4 row(s) containing missing values (geom_path).## Warning: Removed 26 rows containing missing values (geom_point).
##
## $`sp|P01019|ANGT_HUMAN (Oxidation (M)) 105`## Warning: Removed 143 row(s) containing missing values (geom_path).## Warning: Removed 252 rows containing missing values (geom_point).
##
## $`sp|P01042|KNG1_HUMAN (Phospho (STY)) 275`## Warning: Removed 6 row(s) containing missing values (geom_path).## Warning: Removed 102 rows containing missing values (geom_point).
##
## $`sp|P02765|FETUA_HUMAN (Oxidation (M)) 321`## Warning: Removed 22 rows containing missing values (geom_point).
##
## $`sp|P05060|SCG1_HUMAN (Phospho (STY)) 311`## Warning: Removed 1 row(s) containing missing values (geom_path).## Warning: Removed 60 rows containing missing values (geom_point).
##
## $`sp|P05060|SCG1_HUMAN (Phospho (STY)) 317`## Warning: Removed 7 row(s) containing missing values (geom_path).## Warning: Removed 97 rows containing missing values (geom_point).
##
## $`sp|P10451|OSTP_HUMAN (Oxidation (M)) 284`## Warning: Removed 37 row(s) containing missing values (geom_path).## Warning: Removed 290 rows containing missing values (geom_point).
##
## $`sp|P10451|OSTP_HUMAN (Phospho (STY)) 280`## Warning: Removed 28 row(s) containing missing values (geom_path).## Warning: Removed 267 rows containing missing values (geom_point).
##
## $`sp|P10909|CLUS_HUMAN (Phospho (STY)) 210`## Warning: Removed 104 rows containing missing values (geom_point).
##
## $`sp|P62328|TYB4_HUMAN (Acetyl (Protein N-term)) 1`## Warning: Removed 26 row(s) containing missing values (geom_path).## Warning: Removed 144 rows containing missing values (geom_point).
##
## $`sp|P07197|NFM_HUMAN (Phospho (STY)) 736`## Warning: Removed 120 rows containing missing values (geom_point).
##
## $`sp|Q14515|SPRL1_HUMAN (Oxidation (M)) 276`## Warning: Removed 26 row(s) containing missing values (geom_path).## Warning: Removed 194 rows containing missing values (geom_point).
5.1.2 Peptidoform level
pepf_list1 <- c()
for (contrast in contrasts){
sigTable <- rowData(pe[["pepformRel"]])[[contrast]] %>% filter(adjPval<0.05)
pepf_list1 <- c(pepf_list1, rownames(sigTable))
}
pepf_list1 <- unique(pepf_list1)
plots <- list()
for (i in pepf_list1){
prot_ = rowData(pe[["pepformRel"]])[i,][["Leading.razor.protein"]]
site_ = rowData(pe[["pepformRel"]])[i,][["mod"]]
sites_ = strsplit(site_, ", ")[[1]]
for (site_ in sites_){
ptm_ = paste(prot_, site_, collapse = " ")
print(ptm_)
pepform_df <- longFormat(pe[,,"pepformRel"], rowvars = c("Leading.razor.protein", "ptm"), colvars = c("condition", "subset")) %>% as.data.frame()
pepform_df <- pepform_df %>% filter(Leading.razor.protein == prot_)
pepform_df <- pepform_df %>% filter(grepl(site_, ptm, fixed = T))
pepform_df$FeatureType <- "Peptide"
pepform_df[pepform_df$rowname==i,]$FeatureType <- "SignificantPeptide"
pepform_df <- pepform_df %>% arrange(condition, subset, rowname)
ptm_df <- longFormat(pe[,,"ptmRel"], rowvars = c("protein", "ptm"), colvars = c("condition", "subset")) %>% as.data.frame()
ptm_df <- ptm_df %>% filter(ptm == ptm_)
ptm_df$FeatureType <- "PTM"
ptm_df <- ptm_df %>% arrange(condition, subset, rowname)
ptm_estimate <- ptm_df %>% select(c("primary", "colname", "condition", "subset")) %>%
mutate(rowname = paste(ptm_df$rowname, "estimate"),
ptm = paste(ptm_df$ptm, "estimate"),
Protein = paste(ptm_df$Protein, "estimate"),
FeatureType = "PTM_estimated",
value = NA,
assay = "model")
fixeffects <- rowData(pe[["ptmRel"]])$msqrobModels[[unique(ptm_df$ptm)]] %>% getCoef
ptm_estimate[ptm_estimate$condition=="B"&(ptm_estimate$subset=="x"),]$value <-
fixeffects[["(Intercept)"]]
ptm_estimate[ptm_estimate$condition=="B"&(ptm_estimate$subset=="y"),]$value <-
fixeffects[["(Intercept)"]] + fixeffects[["subsety"]]
ptm_estimate[ptm_estimate$condition=="A"&(ptm_estimate$subset=="x"),]$value <-
fixeffects[["(Intercept)"]] + fixeffects[["conditionA"]]
ptm_estimate[ptm_estimate$condition=="A"&(ptm_estimate$subset=="y"),]$value <-
fixeffects[["(Intercept)"]] + fixeffects[["conditionA"]] + fixeffects[["conditionA:subsety"]]+ fixeffects[["subsety"]]
plot_df <- rbindlist(list(pepform_df, ptm_df, ptm_estimate), fill = TRUE)
plot_points <- rbindlist(list(pepform_df, ptm_df), fill = TRUE)
# plot_df[plot_df$FeatureType == 'PTM'][['FeatureType']] <- "PTM Summarized"
# plot_df[plot_df$FeatureType != 'PTM'][['FeatureType']] <- "PTM Feature"
plot_df$primary <- forcats::fct_inorder(plot_df$primary)
plots[[paste(i, site_)]] <- plot_df %>% ggplot() +
geom_line(aes(x = primary, y = value , group = rowname, color = FeatureType), size =2) +
geom_point(data = plot_points, aes(x = primary, y = value , group = rowname, color = FeatureType), size = 5) +
geom_vline(data=data.frame(x = c(43.5, 17.5, 56.5)),
aes(xintercept=as.numeric(x)), linetype = "dashed") +
scale_colour_manual(values = c("Peptide" = "#C3C3C3", "PTM" = "palegreen3",
"SignificantPeptide" = "violetred1",
"PTM_estimated" = "lightgoldenrod")) +
labs(title = paste(i, ptm_), x = "Sample", y = "Intensity") +
theme_light() +
theme(axis.text.x = element_text(angle = 60, hjust=1, size = 10),
axis.text.y = element_text(size = 16),
legend.text=element_text(size=19),
axis.title.y = element_text(size = 16),
axis.title.x = element_text(size = 16),
title = element_text(size = 19),
strip.text = element_text(size = 16),
legend.title = element_blank(),
legend.direction = "horizontal",
legend.position = c(0.5, 0.1)) +
annotate("text", x = 24, y = 6.5, label = "B", size = 8) +
annotate("text", x = 70, y = 6.5, label = "A", size = 8) +
annotate("text", x = 9, y = 6, label = "x", size = 7) +
annotate("text", x = 30, y = 6, label = "y", size = 7) +
annotate("text", x = 49.5, y = 6, label = "x", size = 7) +
annotate("text", x = 73, y = 6, label = "y", size = 7) +
ylim(-6, 6.5)
}
}## [1] "sp|O94769|ECM2_HUMAN (Phospho (STY)) 75"## Warning: 'experiments' dropped; see 'metadata'## harmonizing input:
## removing 450 sampleMap rows not in names(experiments)## Warning: 'experiments' dropped; see 'metadata'## harmonizing input:
## removing 450 sampleMap rows not in names(experiments)## [1] "sp|O94769|ECM2_HUMAN (Oxidation (M)) 76"## Warning: 'experiments' dropped; see 'metadata'## harmonizing input:
## removing 450 sampleMap rows not in names(experiments)## Warning: 'experiments' dropped; see 'metadata'## harmonizing input:
## removing 450 sampleMap rows not in names(experiments)## [1] "sp|P01019|ANGT_HUMAN (Oxidation (M)) 105"## Warning: 'experiments' dropped; see 'metadata'## harmonizing input:
## removing 450 sampleMap rows not in names(experiments)## Warning: 'experiments' dropped; see 'metadata'## harmonizing input:
## removing 450 sampleMap rows not in names(experiments)## [1] "sp|P10451|OSTP_HUMAN (Phospho (STY)) 270"## Warning: 'experiments' dropped; see 'metadata'## harmonizing input:
## removing 450 sampleMap rows not in names(experiments)## Warning: 'experiments' dropped; see 'metadata'## harmonizing input:
## removing 450 sampleMap rows not in names(experiments)## [1] "sp|P10451|OSTP_HUMAN (Oxidation (M)) 284"## Warning: 'experiments' dropped; see 'metadata'## harmonizing input:
## removing 450 sampleMap rows not in names(experiments)## Warning: 'experiments' dropped; see 'metadata'## harmonizing input:
## removing 450 sampleMap rows not in names(experiments)## [1] "sp|P10909|CLUS_HUMAN (Phospho (STY)) 210"## Warning: 'experiments' dropped; see 'metadata'## harmonizing input:
## removing 450 sampleMap rows not in names(experiments)## Warning: 'experiments' dropped; see 'metadata'## harmonizing input:
## removing 450 sampleMap rows not in names(experiments)plots## $`_LPIVNFDYS(Phospho (STY))M(Oxidation (M))EEK_ (Phospho (STY)) 75`## Warning: Removed 13 rows containing missing values (geom_point).
##
## $`_LPIVNFDYS(Phospho (STY))M(Oxidation (M))EEK_ (Oxidation (M)) 76`## Warning: Removed 22 rows containing missing values (geom_point).
##
## $`_AAM(Oxidation (M))VGMLANFLGFR_ (Oxidation (M)) 105`## Warning: Removed 143 row(s) containing missing values (geom_path).## Warning: Removed 252 rows containing missing values (geom_point).
##
## $`_VS(Phospho (STY))REFHSHEFHSHEDM(Oxidation (M))LVVDPK_ (Phospho (STY)) 270`## Warning: Removed 35 row(s) containing missing values (geom_path).## Warning: Removed 240 rows containing missing values (geom_point).
##
## $`_VS(Phospho (STY))REFHSHEFHSHEDM(Oxidation (M))LVVDPK_ (Oxidation (M)) 284`## Warning: Removed 37 row(s) containing missing values (geom_path).## Warning: Removed 290 rows containing missing values (geom_point).
##
## $`_EPQDTYHYLPFS(Phospho (STY))LPHR_ (Phospho (STY)) 210`## Warning: Removed 104 rows containing missing values (geom_point).
---
title: "phosphoDataset msqrobPTM without GP"
output:
  html_document:
    code_download: yes
    theme: cosmo
    toc: yes
    toc_depth: 4
    toc_float:
      collapsed: yes
    highlight: tango
    number_sections: yes
    df_print: paged
  pdf_document:
    toc: yes
    toc_depth: '4'
---

```{r, setup, include=FALSE}
knitr::opts_knit$set(root.dir = "C:/Users/Nina/OneDrive - UGent/Documenten/Doctoraat/msqrobPTM paper/biological phospho dataset/")
```

```{r message=FALSE}
library(QFeatures)
library(msqrob2)
library(dplyr)
library(tidyr)
library(tibble)
library(gt)
library(plotly)
library(stageR)
library(poolr)
library(RColorBrewer)
library(seqinr)
library(stringr)
library(ExploreModelMatrix)
library(data.table)
```



# Import data


```{r}
df <- read.csv("evidence.txt",header=TRUE, sep = ",")
#I can ignore the sigma proteins (those were just used for QC purposes)
df <- df %>% filter(!grepl("Sigma", df$Proteins))
#filter out phospho sites with less than 75% probability
#(other filtering options are possible)
filter_rows <- sapply(df$Phospho..STY..Probabilities, function(x){
  if (!x==""){
    prob <- str_extract_all(x,  "(?<=\\().+?(?=\\))")[[1]]
    if (!any(as.double(prob) > 0.75)){return(which(df$Phospho..STY..Probabilities == x))}
  }
}, USE.NAMES = F)
filter_rows <- unique(unlist(filter_rows))
nrow(df)
df <- df[-filter_rows,]
nrow(df)
#Now get format into wide format
df_wide <- pivot_wider(df, id_cols = c("Sequence", "Modifications", "Modified.sequence", "Proteins", "Leading.proteins",
                                        "Reverse", "Potential.contaminant", "Protein.group.IDs", "Leading.razor.protein"),
                                                                          #Is dat hier de beste manier om met 
                                                                          #gedupliceerde features om te gaan?
                       names_from = "Raw.file", names_prefix = "Intensity_", values_from = "Intensity", values_fn = max)
```


```{r}
ecols <- grep("Intensity",colnames(df_wide))
#order dataframe by protein, for the normalisation step
df_wide = df_wide[order(df_wide$Leading.razor.protein),]
pe <- readQFeatures(df_wide,ecol= ecols,name="peptidoformRaw")
```

```{r}
rownames(pe[["peptidoformRaw"]]) <- rowData(pe[["peptidoformRaw"]])$Modified.sequence
```

## Experimental Layout

```{r}
metadata <- read.csv("Experimental Design.csv")
```

```{r}
colData(pe)$file <- sapply(str_split(rownames(colData(pe)), "_"), function(x) x[[2]])
metadata$File <- sub(x=metadata$File, pattern=".raw",replacement = "")
colData(pe)$condition <- sapply(colData(pe)$file, function(x){
  metadata[metadata$File==x,]$Condition
}, USE.NAMES = F)
colData(pe)$subset <- sapply(colData(pe)$file, function(x){
  metadata[metadata$File==x,]$Subset
}, USE.NAMES = F)
colData(pe)$condition <- case_when(grepl("A", colData(pe)$condition) ~ "A",
                                        TRUE ~ "B")
colData(pe)
```


```{r}
MSnbase::plotNA(assay(pe[["peptidoformRaw"]])) +
  xlab("Peptide index (ordered by data completeness)")
```


# Preprocessing


```{r}
rowData(pe[["peptidoformRaw"]])$nNonZero <- rowSums(assay(pe[["peptidoformRaw"]]) > 0, na.rm = T)
pe <- zeroIsNA(pe, i = "peptidoformRaw")
pe <- filterFeatures(pe, ~Reverse != "+")
pe <- filterFeatures(pe, ~Potential.contaminant != "+")
pe <- logTransform(pe, base = 2, i = "peptidoformRaw", name = "peptidoformLog")
pe <- pe[rowData(pe[["peptidoformRaw"]])$nNonZero>2,,]
pe <- QFeatures::normalize(pe, method = "center.median", i = "peptidoformLog", name = "peptidoform")
colData(pe[["peptidoform"]]) <- colData(pe)
```


## Normalisation via robust summarisation


```{r,warning=FALSE}
pe <- aggregateFeatures(pe,
 i = "peptidoform",
 fcol = "Leading.razor.protein",
 na.rm = TRUE,
 name = "proteinRobust",
 fun = MsCoreUtils::robustSummary)
```

```{r}
#Only take pepforms that have a parent protein
#(when there is no global profiling dataset, this will be all peptidoforms)
pepWithProtein <- rowData(pe[["peptidoform"]])$Proteins %in% rownames(pe[["proteinRobust"]])
pePepWithProtein  <- pe[["peptidoform"]][pepWithProtein,]
pe <- addAssay(pe,pePepWithProtein,"pepformRel")
#normalisation for protein abundance step
assay(pe[["pepformRel"]]) <- assay(pe[["pepformRel"]]) - assay(pe[["proteinRobust"]])[rowData(pe[["pepformRel"]])$Proteins,colnames(assay(pe[["pepformRel"]]))]
boxplot(assay(pe[["pepformRel"]]))
```


# Differential peptidoform usage (DPFU)

## Hypothesistest for each contrast

```{r}
colData(pe)$condition <- relevel(as.factor(colData(pe)$condition), ref = "B")
colData(pe)$subset <- as.factor(colData(pe)$subset)
```

```{r}
pe <- msqrob2::msqrob(object = pe, i = "pepformRel", formula = ~condition*subset, robust=FALSE)
rowData(pe[["pepformRel"]])$msqrobModels[[2]] %>%
                  getCoef
```


```{r}
fraction_of_ys <- (colData(pe) %>% as_tibble() %>% filter(subset == "y") %>% nrow()) / nrow(colData(pe))
contrasts <- c("conditionA", "conditionA + conditionA:subsety", "conditionA:subsety", "conditionA + 0.5 * conditionA:subsety", "conditionA + 0.6666667 * conditionA:subsety")
L <- makeContrast(c("conditionA = 0",
                    "conditionA + conditionA:subsety = 0",
                    "conditionA:subsety = 0",
                    "conditionA + 0.5 * conditionA:subsety = 0",
                    "conditionA + 0.6666667 * conditionA:subsety = 0"),
                  parameterNames = rowData(pe[["pepformRel"]])$msqrobModels[[2]] %>%
                  getCoef %>%
                  names)
pe <- hypothesisTest(object = pe, i = "pepformRel", contrast = L, overwrite = T)
```


### Volcanoplot 

```{r}
volcano <- list()
for (contrast in contrasts){
volcano[[contrast]] <- rowData(pe[["pepformRel"]])[[contrast]]%>%
            ggplot(aes(x = logFC, y = -log10(pval), 
                   color = adjPval < 0.05,
                   annotation=rowData(pe[["pepformRel"]])[,3])) +
  geom_point(cex = 2.5) +
  scale_color_manual(values = alpha(c("black", "red"), 0.5)) + 
  theme_minimal() +
  ylab("-log10(pvalue)") +
  ggtitle(contrast)
}
volcano
```

```{r eval=FALSE, fig.height=4, fig.width=12, include=FALSE}
for(contrast in contrasts){
sigNames <- rowData(pe[["pepformRel"]])[[contrast]] %>%
 rownames_to_column("peptidoform") %>%
 filter(adjPval<0.05) %>%
 pull(peptidoform)
heatmap(assay(pe[["pepformRel"]])[sigNames, ], main = contrast)
}
```

### Significant peptidoforms for each contrast


```{r}
tables <- list()
for (contrast in contrasts){
sigTable <- rowData(pe[["pepformRel"]])[[contrast]]
if(nrow(sigTable %>%
  na.exclude %>%
  filter(adjPval<0.05))>0){
sigTable <- sigTable %>%
  na.exclude %>%
  filter(adjPval<0.05) %>%
  arrange(pval) %>%
  mutate(
    se = format(se, digits = 2), 
    df = format(df, digits =2),
    t = format(t, digits = 2),
    adjPval = format(adjPval, digits = 3),
    rank = 1:length(logFC) 
  ) 
sigTable_print <- sigTable %>% mutate(peptidoform = rownames(sigTable)) %>% gt() %>% tab_header(title = md(contrast))
tables[[contrast]] <- sigTable
}
}
knitr::kable(tables)
```

```{r eval=FALSE, include=FALSE}
tables_toCsv <- do.call("rbind", tables)
tables_toCsv <- tables_toCsv %>% rownames_to_column("contrast")
pepforms <- sapply(unique(tables_toCsv$contrast), function(x){rownames(tables[[x]])}, USE.NAMES = F)
tables_toCsv$Peptidoform <- pepforms
write.csv(tables_toCsv, file = "C:/Users/Nina/OneDrive - UGent/Documenten/Doctoraat/msqrobPTM/PhosphoDataset /significance_tables_peptidoform.csv")
```


# DPTM

## ptm summarisatie

### Determine location of ptm

```{r}
fasta <- "human.fasta"
parsed_fasta <- read.fasta(file = fasta, seqtype = "AA", as.string = T)
```

  
```{r}
#modified sequence column contains _ that does not matter, but hinders location determining
rowData(pe[["pepformRel"]])$modified_sequence <- gsub("_", "", rowData(pe[["pepformRel"]])$Modified.sequence)
```

  

```{r}
get_ptm_location <- function(feature, data, fasta, mod_column = "Modifications", 
                             peptide_seq_column = "Sequence", mod_seq_column = "modified_sequence", 
                             protein_column = "Leading.razor.protein", split = ",", collapse = ", "){
  prot <- data[feature,][[protein_column]]
  pep_seq <- data[feature,][[peptide_seq_column]]
  mod_seq <- data[feature,][[mod_seq_column]]
  prot_seq <- fasta[[prot]][1]

  #find location of peptide in protein
  #add -1 here, so that the addition of the location later on is correct
  pep_location <- unlist(lapply(gregexpr(pattern = pep_seq, prot_seq), min)) - 1
  final_mod <- c()
  j <- mod_seq
  #go over the modifications inside the modified sequence
  for(mod in regmatches(mod_seq, gregexpr("\\(.*?\\)\\)", mod_seq, perl=T))[[1]]){
    #find location of modification in peptide
    mod_location <- unlist(lapply(gregexpr(pattern = mod, j, fixed = T), min))
    #get location in protein (-1 because else it gives you the location after because of the presence of the modification in the string)
    location <- mod_location + pep_location -1
    #add location to modification
    mod_ <- paste(mod, location)
    #save modification
    final_mod <- c(final_mod, mod_)
    #now remove current modification from the sequence, so that we can continue to the next mod
    str_sub(j, mod_location, nchar(mod)+mod_location-1) <- ""
  }
  return(paste(final_mod, collapse = collapse))
}
```

```{r}
rowData(pe[["pepformRel"]])$mod <- sapply(rownames(rowData(pe[["pepformRel"]])), function(x){
    get_ptm_location(x, rowData(pe[["pepformRel"]]), parsed_fasta)
}, USE.NAMES = F)
```


```{r}
#Add ptm variable = protein + modification
rowData(pe[["pepformRel"]])$ptm <- ifelse(rowData(pe[["pepformRel"]])$mod != "",
                                               paste(rowData(pe[["pepformRel"]])$Leading.razor.protein, rowData(pe[["pepformRel"]])$mod, sep="_"), 
                                               "")
```

### Get ptm level intensity matrix

Get all unique ptms present in the dataset (all protein - modification - location combinations)

```{r}
prots <- unique(rowData(pe[["pepformRel"]])$Leading.razor.protein)
#Do for each protein
ptms <- sapply(prots, function(i) {
  pe_sub <- pe[["pepformRel"]][grepl(i, rowData(pe[["pepformRel"]])$Leading.razor.protein, fixed = T),]
  #pe_sub <- filterFeatures(pe,~grepl(Leading.razor.protein,pattern=i,fixed = T))
  #Get all unique modification present on that protein
  mods <- unique(unlist(strsplit(rowData(pe_sub)$mod, split = ", ", fixed = TRUE)))
  #Add protein info to mods
  ptm <- paste(rep(i, length(mods)), mods)
  #return all the protein-mods combinations
  ptm
})
ptms <- as.vector(unlist(ptms))
```



```{r}
#For each ptm do
ptm_x_assay <- sapply(seq(1:length(ptms)), function(i){ 
  x <- ptms[i]
  #Get current protein and mod from ptm
  prot <- str_split(x, " ", 2)[[1]][1]
  current_ptm <- str_split(x, " ", 2)[[1]][2]
  #filter on that protein and on that mod to obtain all peptidoforms that correspond to the ptm
  pe_sub <- pe[["pepformRel"]][grepl(prot, rowData(pe[["pepformRel"]])$Leading.razor.protein, fixed = T),]
  #pe_sub <- filterFeatures(pe,~grepl(Leading.razor.protein,pattern=prot, fixed = T))
  ptm_sub <- pe_sub[grepl(current_ptm, rowData(pe_sub)$mod, fixed = T),]
  #ptm_sub <- filterFeatures(pe_sub,~grepl(mod,pattern=current_ptm, fixed=T))[["peptidoformNorm"]]
  #Get intensity values of those peptidoforms
  y <- assay(ptm_sub)
  #And summarise them to 1 row of intensity values: 1 value per sample for that ptm
  ptm_y <- try(MsCoreUtils::robustSummary(y), silent = T)
  if (is(ptm_y, "try-error")){
    ptm_y <- rep(NA, ncol(y))}
  ptm_y
})
#Then we get the intensity assay on ptm level
ptm_x_assay <- t(ptm_x_assay)
rownames(ptm_x_assay) <- ptms
```

### Add to QFeatures object

Filter out ptms with all zero intensities

```{r}
print(paste(nrow(ptm_x_assay), "ptms before filtering"))
filtering <- rowSums(ptm_x_assay != 0, na.rm=TRUE) > 0 
ptm_x_assay <- ptm_x_assay[filtering,]
print(paste(nrow(ptm_x_assay), "ptms after filtering"))
```

```{r}
all(rownames(colData(pe)) == colnames(ptm_x_assay))
rowdata <- data.frame(ptm = rownames(ptm_x_assay))
rowdata$protein <- sapply(str_split(rowdata$ptm, pattern=" "),function(x) x[1])
ptm_y_assay <- SummarizedExperiment(assays=as.matrix(ptm_x_assay), rowData=rowdata, colData=colData(pe))
```


```{r}
pe <- addAssay(pe, ptm_y_assay, "ptmRel")
```



## Hypothesistest for each contrast


```{r}
pe <- msqrob2::msqrob(object = pe, i = "ptmRel", formula = ~condition*subset, robust=TRUE, overwrite = T)
rowData(pe[["ptmRel"]])$msqrobModels[[2]] %>%
                  getCoef
```



```{r}
contrasts <- c("conditionA", "conditionA + conditionA:subsety", "conditionA:subsety", "conditionA + 0.5 * conditionA:subsety", "conditionA + 0.6666667 * conditionA:subsety")
L <- makeContrast(c("conditionA = 0",
                    "conditionA + conditionA:subsety = 0",
                    "conditionA:subsety = 0",
                    "conditionA + 0.5 * conditionA:subsety = 0",
                    "conditionA + 0.6666667 * conditionA:subsety = 0"),   
                  parameterNames = rowData(pe[["ptmRel"]])$msqrobModels[[2]] %>%
                  getCoef %>%
                  names)
pe <- hypothesisTest(object = pe, i = "ptmRel", contrast = L, overwrite = T)
```



### Volcanoplot 


```{r}
volcanos <- list()
for (contrast in contrasts){
library(plotly)
volcanos[[contrast]] <- 
  rowData(pe[["ptmRel"]])[[contrast]]%>%
  ggplot(aes(x = logFC, y = -log10(pval), 
             color = adjPval < 0.05,
             annotation=rowData(pe[["ptmRel"]])[,3])) +
  geom_point(cex = 2.5) +
  scale_color_manual(values = alpha(c("black", "red"), 0.5)) + theme_minimal() +
  ylab("-log10(pvalue)") +
  ggtitle(contrast)
}
volcanos
```

### Heatmap

We first select the names of the ptms that were declared signficant.

```{r eval=FALSE, include=FALSE}
for(contrast in contrasts){
sigNames <- rowData(pe[["ptmRel"]])[[contrast]] %>%
 rownames_to_column("ptmRel") %>%
 filter(adjPval<0.05) %>%
 pull(ptm)
heatmap(assay(pe[["ptmRel"]])[sigNames, ], main = contrast)
}
```


### Significant ptms for each contrast


```{r}
tables <- list()
for (contrast in contrasts){
sigTable <- rowData(pe[["ptmRel"]])[[contrast]]
if(nrow(sigTable %>%
  na.exclude %>%
  filter(adjPval<0.05)) > 0){
sigTable <- sigTable %>%
  na.exclude %>%
  filter(adjPval<0.05) %>%
  arrange(pval) %>%
  mutate(
    se = format(se, digits = 2), 
    df = format(df, digits =2),
    t = format(t, digits = 2),
    adjPval = format(adjPval, digits = 3),
    rank = 1:length(logFC) 
  ) 
sigTable_print <- sigTable %>% mutate(peptidoform = rownames(sigTable)) %>% gt() %>% tab_header(title = md(contrast))
tables[[contrast]] <- sigTable
}
}
knitr::kable(tables)
```

```{r eval=FALSE, include=FALSE}
tables_toCsv <- do.call("rbind", tables)
tables_toCsv <- tables_toCsv %>% rownames_to_column("rowname")
write.csv(tables_toCsv, file = "significance_tables_PTM.csv")
```


```{r}
a = "sp|Q16610|ECM1_HUMAN (Phospho (STY)) 80"
b = "sp|P13611|CSPG2_HUMAN (Phospho (STY)) 2115"
tables <- list()
for (contrast in contrasts){
sigTable <- rowData(pe[["ptmRel"]])[[contrast]]
tables[[contrast]] <- sigTable %>% rownames_to_column("ptm") %>% filter(ptm==a|ptm==b)
}
knitr::kable(tables)
```

# PLOTS

## Lineplots

### PTM - level

```{r, fig.width=18, fig.height=10}
ptm_list1 <- c()
for (contrast in contrasts){
sigTable <- rowData(pe[["ptmRel"]])[[contrast]] %>% filter(adjPval<0.05)
ptm_list1 <- c(ptm_list1, rownames(sigTable))
}
ptm_list1 <- unique(ptm_list1)

plots <- list()
for (i in ptm_list1){
prot_ = str_split(i, " ")[[1]][1]
site_ = str_split_fixed(i, " ", 2)[,2]

pepform_df <- longFormat(pe[,,"pepformRel"], rowvars = c("Leading.razor.protein", "ptm"), colvars = c("condition", "subset")) %>% as.data.frame()
pepform_df <- pepform_df %>% filter(Leading.razor.protein == prot_)
pepform_df <- pepform_df %>% filter(grepl(site_, ptm, fixed = T))
pepform_df$FeatureType <- "Peptide"
pepform_df <- pepform_df %>% arrange(condition, subset, rowname)

ptm_df <- longFormat(pe[,,"ptmRel"], rowvars = c("protein", "ptm"), colvars = c("condition", "subset")) %>% as.data.frame()
ptm_df <- ptm_df %>% filter(ptm == i)
ptm_df$FeatureType <- "PTM"
ptm_df <- ptm_df %>% arrange(condition, subset, rowname)

ptm_estimate <- ptm_df %>% select(c("primary", "colname", "condition", "subset")) %>%
                           mutate(rowname = paste(ptm_df$rowname, "estimate"),
                                  ptm = paste(ptm_df$ptm, "estimate"),
                                  Protein = paste(ptm_df$Protein, "estimate"),
                                  FeatureType = "PTM_estimated",
                                  value = NA,
                                  assay = "model")
fixeffects <- rowData(pe[["ptmRel"]])$msqrobModels[[unique(ptm_df$ptm)]] %>% getCoef
ptm_estimate[ptm_estimate$condition=="B"&(ptm_estimate$subset=="x"),]$value <- 
                                                    fixeffects[["(Intercept)"]]
ptm_estimate[ptm_estimate$condition=="B"&(ptm_estimate$subset=="y"),]$value <- 
   fixeffects[["(Intercept)"]] + fixeffects[["subsety"]]
ptm_estimate[ptm_estimate$condition=="A"&(ptm_estimate$subset=="x"),]$value <- 
   fixeffects[["(Intercept)"]] + fixeffects[["conditionA"]]
ptm_estimate[ptm_estimate$condition=="A"&(ptm_estimate$subset=="y"),]$value <- 
   fixeffects[["(Intercept)"]] + fixeffects[["conditionA"]] + fixeffects[["conditionA:subsety"]] + fixeffects[["subsety"]]

plot_df <- rbindlist(list(pepform_df, ptm_df, ptm_estimate), fill = TRUE)
plot_points <- rbindlist(list(pepform_df, ptm_df), fill = TRUE)

plot_df$primary <- forcats::fct_inorder(plot_df$primary)

plots[[i]] <- plot_df %>% ggplot() +
  geom_line(aes(x = primary, y = value , group = rowname, color = FeatureType), size = 2) +
  geom_point(data = plot_points, aes(x = primary, y = value , group = rowname, 
                                     color = FeatureType), size = 5) +
  geom_vline(data=data.frame(x = c(43.5, 17.5, 56.5)),
             aes(xintercept=as.numeric(x)), linetype = "dashed") +
  scale_colour_manual(values = c("Peptide" = "#C3C3C3", "PTM" = "palegreen3",
                                 "PTM_estimated" = "slateblue3")) +
  labs(title = i, x = "Sample", y = "Intensity") +
  theme_light() +
  theme(axis.text.x = element_text(angle = 60, hjust=1, size = 10),
        axis.text.y = element_text(size = 16),
        legend.text=element_text(size=19),
        axis.title.y = element_text(size = 16),
        axis.title.x = element_text(size = 16),
        title = element_text(size = 19),
        strip.text = element_text(size = 16),
        legend.title =  element_blank(),
        legend.direction = "horizontal",
        legend.position = c(0.5, 0.1)) +
  annotate("text", x = 24, y = 6.5, label = "B", size = 8) +
  annotate("text", x = 70, y = 6.5, label = "A", size = 8) +
  annotate("text", x = 9, y = 6, label = "x", size = 7) +
  annotate("text", x = 30, y = 6, label = "y", size = 7) +
  annotate("text", x = 49.5, y = 6, label = "x", size = 7) +
  annotate("text", x = 73, y = 6, label = "y", size = 7) +
  ylim(-6, 6.5)

}
plots
```


### Peptidoform level



```{r, fig.width=18, fig.height=10}
pepf_list1 <- c()
for (contrast in contrasts){
sigTable <- rowData(pe[["pepformRel"]])[[contrast]] %>% filter(adjPval<0.05)
pepf_list1 <- c(pepf_list1, rownames(sigTable))
}
pepf_list1 <- unique(pepf_list1)

plots <- list()
for (i in pepf_list1){
prot_ = rowData(pe[["pepformRel"]])[i,][["Leading.razor.protein"]]
site_ = rowData(pe[["pepformRel"]])[i,][["mod"]]
sites_ = strsplit(site_, ", ")[[1]]
for (site_ in sites_){
ptm_ = paste(prot_, site_, collapse = " ")
print(ptm_)

pepform_df <- longFormat(pe[,,"pepformRel"], rowvars = c("Leading.razor.protein", "ptm"), colvars = c("condition", "subset")) %>% as.data.frame()
pepform_df <- pepform_df %>% filter(Leading.razor.protein == prot_)
pepform_df <- pepform_df %>% filter(grepl(site_, ptm, fixed = T))
pepform_df$FeatureType <- "Peptide"
pepform_df[pepform_df$rowname==i,]$FeatureType <- "SignificantPeptide"
pepform_df <- pepform_df %>% arrange(condition, subset, rowname)

ptm_df <- longFormat(pe[,,"ptmRel"], rowvars = c("protein", "ptm"), colvars = c("condition", "subset")) %>% as.data.frame()
ptm_df <- ptm_df %>% filter(ptm == ptm_)
ptm_df$FeatureType <- "PTM"
ptm_df <- ptm_df %>% arrange(condition, subset, rowname)

ptm_estimate <- ptm_df %>% select(c("primary", "colname", "condition", "subset")) %>%
                           mutate(rowname = paste(ptm_df$rowname, "estimate"),
                                  ptm = paste(ptm_df$ptm, "estimate"),
                                  Protein = paste(ptm_df$Protein, "estimate"),
                                  FeatureType = "PTM_estimated",
                                  value = NA,
                                  assay = "model")
fixeffects <- rowData(pe[["ptmRel"]])$msqrobModels[[unique(ptm_df$ptm)]] %>% getCoef
ptm_estimate[ptm_estimate$condition=="B"&(ptm_estimate$subset=="x"),]$value <- 
                                                    fixeffects[["(Intercept)"]]
ptm_estimate[ptm_estimate$condition=="B"&(ptm_estimate$subset=="y"),]$value <- 
   fixeffects[["(Intercept)"]] + fixeffects[["subsety"]]
ptm_estimate[ptm_estimate$condition=="A"&(ptm_estimate$subset=="x"),]$value <- 
   fixeffects[["(Intercept)"]] + fixeffects[["conditionA"]]
ptm_estimate[ptm_estimate$condition=="A"&(ptm_estimate$subset=="y"),]$value <- 
   fixeffects[["(Intercept)"]] + fixeffects[["conditionA"]] + fixeffects[["conditionA:subsety"]]+ fixeffects[["subsety"]]


plot_df <- rbindlist(list(pepform_df, ptm_df, ptm_estimate), fill = TRUE)
plot_points <- rbindlist(list(pepform_df, ptm_df), fill = TRUE)

# plot_df[plot_df$FeatureType == 'PTM'][['FeatureType']] <- "PTM Summarized"
# plot_df[plot_df$FeatureType != 'PTM'][['FeatureType']] <- "PTM Feature"
plot_df$primary <- forcats::fct_inorder(plot_df$primary)

plots[[paste(i, site_)]] <- plot_df %>% ggplot() +
  geom_line(aes(x = primary, y = value , group = rowname, color = FeatureType), size =2) +
  geom_point(data = plot_points, aes(x = primary, y = value , group = rowname, color = FeatureType), size = 5) +
  geom_vline(data=data.frame(x = c(43.5, 17.5, 56.5)),
             aes(xintercept=as.numeric(x)), linetype = "dashed") +
  scale_colour_manual(values = c("Peptide" = "#C3C3C3", "PTM" = "palegreen3", 
                                 "SignificantPeptide" = "violetred1",
                                 "PTM_estimated" = "lightgoldenrod")) +
  labs(title = paste(i, ptm_), x = "Sample", y = "Intensity") +
  theme_light() +
  theme(axis.text.x = element_text(angle = 60, hjust=1, size = 10),
        axis.text.y = element_text(size = 16),
        legend.text=element_text(size=19),
        axis.title.y = element_text(size = 16),
        axis.title.x = element_text(size = 16),
        title = element_text(size = 19),
        strip.text = element_text(size = 16),
        legend.title =  element_blank(),
        legend.direction = "horizontal",
        legend.position = c(0.5, 0.1)) +
  annotate("text", x = 24, y = 6.5, label = "B", size = 8) +
  annotate("text", x = 70, y = 6.5, label = "A", size = 8) +
  annotate("text", x = 9, y = 6, label = "x", size = 7) +
  annotate("text", x = 30, y = 6, label = "y", size = 7) +
  annotate("text", x = 49.5, y = 6, label = "x", size = 7) +
  annotate("text", x = 73, y = 6, label = "y", size = 7) +
  ylim(-6, 6.5)
}
}
plots
```

